{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30bbe8c1",
   "metadata": {},
   "source": [
    "# 8. Advanced Topics in Fed-MVKM\n",
    "\n",
    "This section covers advanced topics and provides deeper insights into the Fed-MVKM algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd61b35",
   "metadata": {},
   "source": [
    "## 8.1 Parameter Tuning Guide\n",
    "\n",
    "Understanding and tuning the hyperparameters is crucial for optimal performance.\n",
    "\n",
    "### Key Parameters:\n",
    "\n",
    "1. **alpha (View Weight Control)**\n",
    "   - Controls the influence of each view\n",
    "   - Higher values → More extreme view weights\n",
    "   - Lower values → More balanced view weights\n",
    "   - Recommended range: [5.0, 20.0]\n",
    "\n",
    "2. **beta (Distance Control)**\n",
    "   - Controls the sensitivity to distances\n",
    "   - Higher values → Sharper cluster boundaries\n",
    "   - Lower values → Softer cluster boundaries\n",
    "   - Recommended range: [0.1, 10.0]\n",
    "\n",
    "3. **gamma (Model Update Rate)**\n",
    "   - Controls how quickly the global model updates\n",
    "   - Higher values → Faster updates but potential instability\n",
    "   - Lower values → Slower but more stable updates\n",
    "   - Recommended range: [0.01, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9354559",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: Grid search for optimal parameters\n",
    "def parameter_grid_search(views, labels, n_clients=2):\n",
    "    alphas = [10.0, 15.0, 20.0]\n",
    "    betas = [0.5, 1.0, 2.0]\n",
    "    gammas = [0.02, 0.04, 0.06]\n",
    "    \n",
    "    best_metrics = {'silhouette_avg': -1}\n",
    "    best_params = {}\n",
    "    \n",
    "    for alpha in alphas:\n",
    "        for beta in betas:\n",
    "            for gamma in gammas:\n",
    "                print(f\"\\nTesting parameters: alpha={alpha}, beta={beta}, gamma={gamma}\")\n",
    "                \n",
    "                # Configure model\n",
    "                config = FedMVKMEDConfig(\n",
    "                    cluster_num=len(np.unique(labels)),\n",
    "                    points_view=len(views),\n",
    "                    alpha=alpha,\n",
    "                    beta=beta,\n",
    "                    gamma=gamma,\n",
    "                    max_iterations=10,\n",
    "                    verbose=False\n",
    "                )\n",
    "                \n",
    "                # Create client partitions\n",
    "                client_data, client_labels = create_client_partitions(views, labels, n_clients)\n",
    "                \n",
    "                # Train model\n",
    "                model = FedMVKMED(config)\n",
    "                model.fit(client_data)\n",
    "                predictions = model.predict(client_data)\n",
    "                \n",
    "                # Evaluate\n",
    "                all_pred = np.concatenate([pred for pred in predictions.values()])\n",
    "                metrics = MVKMEDMetrics.compute_metrics(views, all_pred, labels)\n",
    "                \n",
    "                # Update best parameters if improved\n",
    "                if metrics['silhouette_avg'] > best_metrics['silhouette_avg']:\n",
    "                    best_metrics = metrics\n",
    "                    best_params = {'alpha': alpha, 'beta': beta, 'gamma': gamma}\n",
    "                    \n",
    "                print(f\"Silhouette score: {metrics['silhouette_avg']:.4f}\")\n",
    "    \n",
    "    return best_params, best_metrics\n",
    "\n",
    "# Run grid search\n",
    "best_params, best_metrics = parameter_grid_search(views, labels)\n",
    "print(\"\\nBest parameters found:\")\n",
    "print(json.dumps(best_params, indent=2))\n",
    "print(\"\\nBest metrics achieved:\")\n",
    "print(json.dumps(best_metrics, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7618d5a3",
   "metadata": {},
   "source": [
    "## 8.2 Algorithm Internals\n",
    "\n",
    "Understanding how Fed-MVKM works internally:\n",
    "\n",
    "1. **Client-Side Operations**\n",
    "   - Local model updates\n",
    "   - View weight computation\n",
    "   - Privacy preservation\n",
    "   \n",
    "2. **Server-Side Operations**\n",
    "   - Model aggregation\n",
    "   - Global parameter updates\n",
    "   \n",
    "3. **Communication Protocol**\n",
    "   - Only model parameters are shared\n",
    "   - Data never leaves clients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c85fa32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualization of client-server communication\n",
    "def plot_communication_flow(model, iteration=0):\n",
    "    \"\"\"Visualize the communication flow between clients and server.\"\"\"\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    \n",
    "    # Get client and global parameters\n",
    "    client_centers = model.history.get('client_centers', {}).get(iteration, {})\n",
    "    global_centers = model.history.get('center_updates', [])[iteration]\n",
    "    \n",
    "    # Plot parameters for each view\n",
    "    n_views = len(global_centers)\n",
    "    n_clients = len(client_centers)\n",
    "    \n",
    "    for view_idx in range(n_views):\n",
    "        plt.subplot(1, n_views, view_idx + 1)\n",
    "        \n",
    "        # Plot client centers\n",
    "        for client_id, centers in client_centers.items():\n",
    "            plt.scatter(centers[view_idx][:, 0], centers[view_idx][:, 1],\n",
    "                       alpha=0.5, label=f'Client {client_id}')\n",
    "        \n",
    "        # Plot global centers\n",
    "        plt.scatter(global_centers[view_idx][:, 0], global_centers[view_idx][:, 1],\n",
    "                   color='red', marker='*', s=200, label='Global')\n",
    "        \n",
    "        plt.title(f'View {view_idx + 1} Centers')\n",
    "        plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Plot communication flow for a specific iteration\n",
    "if hasattr(model, 'history') and 'center_updates' in model.history:\n",
    "    plot_communication_flow(model, iteration=5)  # Show middle iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e17ad301",
   "metadata": {},
   "source": [
    "## 8.3 Handling Data Heterogeneity\n",
    "\n",
    "In real-world federated scenarios, data is often heterogeneous across clients. Here's how to handle various scenarios:\n",
    "\n",
    "1. **Non-IID Data Distribution**\n",
    "   - Different class distributions per client\n",
    "   - Varying data quality\n",
    "   - Missing views\n",
    "\n",
    "2. **Imbalanced Client Data**\n",
    "   - Different amounts of data per client\n",
    "   - Weighted contribution to global model\n",
    "\n",
    "3. **Privacy-Preserving Techniques**\n",
    "   - Differential privacy\n",
    "   - Secure aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2354afa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: Analyzing data heterogeneity\n",
    "def analyze_data_heterogeneity(client_data, client_labels):\n",
    "    \"\"\"Analyze heterogeneity in client data distributions.\"\"\"\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    \n",
    "    # 1. Class distribution per client\n",
    "    plt.subplot(1, 3, 1)\n",
    "    for client_id, labels in client_labels.items():\n",
    "        unique, counts = np.unique(labels, return_counts=True)\n",
    "        plt.bar(unique + 0.1 * client_id, counts, \n",
    "               alpha=0.5, label=f'Client {client_id}')\n",
    "    plt.title('Class Distribution per Client')\n",
    "    plt.xlabel('Class')\n",
    "    plt.ylabel('Count')\n",
    "    plt.legend()\n",
    "    \n",
    "    # 2. Data volume per client\n",
    "    plt.subplot(1, 3, 2)\n",
    "    volumes = [len(labels) for labels in client_labels.values()]\n",
    "    plt.pie(volumes, labels=[f'Client {i}' for i in range(len(volumes))],\n",
    "            autopct='%1.1f%%')\n",
    "    plt.title('Data Volume Distribution')\n",
    "    \n",
    "    # 3. Feature statistics per client\n",
    "    plt.subplot(1, 3, 3)\n",
    "    for client_id, views in client_data.items():\n",
    "        for view_idx, view in enumerate(views):\n",
    "            means = np.mean(view, axis=0)\n",
    "            plt.boxplot(means, positions=[client_id + view_idx * 0.2],\n",
    "                       labels=[f'C{client_id}V{view_idx}'])\n",
    "    plt.title('Feature Statistics per Client/View')\n",
    "    plt.xlabel('Client-View')\n",
    "    plt.ylabel('Feature Mean')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Analyze heterogeneity in our data\n",
    "analyze_data_heterogeneity(client_data, client_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704504ed",
   "metadata": {},
   "source": [
    "## 8.4 Performance Optimization\n",
    "\n",
    "Strategies to optimize Fed-MVKM performance:\n",
    "\n",
    "1. **Computational Optimization**\n",
    "   - GPU acceleration\n",
    "   - Batch processing\n",
    "   - Parallel client updates\n",
    "\n",
    "2. **Memory Optimization**\n",
    "   - Sparse matrix operations\n",
    "   - Incremental updates\n",
    "   - Memory-efficient data structures\n",
    "\n",
    "3. **Communication Optimization**\n",
    "   - Model compression\n",
    "   - Selective parameter sharing\n",
    "   - Asynchronous updates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f85d7bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: Benchmarking different optimization strategies\n",
    "def benchmark_optimization_strategies(views, labels, n_clients=2):\n",
    "    \"\"\"Compare performance of different optimization strategies.\"\"\"\n",
    "    import time\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # 1. Basic CPU execution\n",
    "    start_time = time.time()\n",
    "    config = FedMVKMEDConfig(\n",
    "        cluster_num=len(np.unique(labels)),\n",
    "        points_view=len(views),\n",
    "        alpha=15.0,\n",
    "        beta=1.0,\n",
    "        gamma=0.04,\n",
    "        device=\"cpu\"\n",
    "    )\n",
    "    model_cpu = FedMVKMED(config)\n",
    "    client_data, _ = create_client_partitions(views, labels, n_clients)\n",
    "    model_cpu.fit(client_data)\n",
    "    results['cpu'] = time.time() - start_time\n",
    "    \n",
    "    # 2. GPU execution (if available)\n",
    "    if torch.cuda.is_available():\n",
    "        start_time = time.time()\n",
    "        config.device = \"cuda\"\n",
    "        model_gpu = FedMVKMED(config)\n",
    "        model_gpu.fit(client_data)\n",
    "        results['gpu'] = time.time() - start_time\n",
    "    \n",
    "    # 3. Memory-efficient execution\n",
    "    start_time = time.time()\n",
    "    config.use_sparse = True\n",
    "    model_sparse = FedMVKMED(config)\n",
    "    model_sparse.fit(client_data)\n",
    "    results['sparse'] = time.time() - start_time\n",
    "    \n",
    "    # Plot results\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.bar(results.keys(), results.values())\n",
    "    plt.title('Execution Time Comparison')\n",
    "    plt.xlabel('Strategy')\n",
    "    plt.ylabel('Time (seconds)')\n",
    "    for i, (k, v) in enumerate(results.items()):\n",
    "        plt.text(i, v, f'{v:.2f}s', ha='center', va='bottom')\n",
    "    plt.show()\n",
    "    \n",
    "    return results\n",
    "\n",
    "# Run benchmark\n",
    "optimization_results = benchmark_optimization_strategies(views, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd70033",
   "metadata": {},
   "source": [
    "## 8.5 Troubleshooting Guide\n",
    "\n",
    "Common issues and their solutions:\n",
    "\n",
    "1. **Convergence Issues**\n",
    "   - Unstable objective values\n",
    "   - Oscillating cluster assignments\n",
    "   - Local optima\n",
    "\n",
    "2. **Memory Issues**\n",
    "   - Out of memory errors\n",
    "   - GPU memory management\n",
    "   - Large dataset handling\n",
    "\n",
    "3. **Quality Issues**\n",
    "   - Poor clustering results\n",
    "   - Imbalanced view weights\n",
    "   - Inconsistent client models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec10358b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diagnose_convergence_issues(model):\n",
    "    \"\"\"Analyze and visualize potential convergence issues.\"\"\"\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    \n",
    "    # 1. Objective value stability\n",
    "    plt.subplot(1, 3, 1)\n",
    "    objectives = model.history['objective_values']\n",
    "    plt.plot(objectives)\n",
    "    plt.title('Objective Value Convergence')\n",
    "    plt.xlabel('Iteration')\n",
    "    plt.ylabel('Objective Value')\n",
    "    \n",
    "    # Calculate stability metrics\n",
    "    obj_diff = np.diff(objectives)\n",
    "    plt.axhline(y=model.config.convergence_threshold, \n",
    "                color='r', linestyle='--', label='Threshold')\n",
    "    plt.legend()\n",
    "    \n",
    "    # 2. View weight stability\n",
    "    plt.subplot(1, 3, 2)\n",
    "    view_weights = np.array(model.history['view_weights'])\n",
    "    for i in range(view_weights.shape[1]):\n",
    "        plt.plot(view_weights[:, i], label=f'View {i+1}')\n",
    "    plt.title('View Weight Stability')\n",
    "    plt.xlabel('Iteration')\n",
    "    plt.ylabel('Weight')\n",
    "    plt.legend()\n",
    "    \n",
    "    # 3. Client model divergence\n",
    "    plt.subplot(1, 3, 3)\n",
    "    client_objectives = model.history['client_objectives']\n",
    "    for client_id, obj in client_objectives.items():\n",
    "        plt.plot(obj, label=f'Client {client_id}')\n",
    "    plt.title('Client Model Divergence')\n",
    "    plt.xlabel('Iteration')\n",
    "    plt.ylabel('Client Objective')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # Print diagnostic information\n",
    "    print(\"\\nDiagnostic Information:\")\n",
    "    print(f\"Objective value range: [{min(objectives):.4f}, {max(objectives):.4f}]\")\n",
    "    print(f\"Maximum objective change: {max(abs(obj_diff)):.4f}\")\n",
    "    print(f\"Final view weights: {view_weights[-1]}\")\n",
    "    print(f\"Number of iterations: {len(objectives)}\")\n",
    "\n",
    "# Run diagnostics\n",
    "diagnose_convergence_issues(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36717cc5",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This advanced section provided:\n",
    "1. Detailed parameter tuning strategies\n",
    "2. Insights into algorithm internals\n",
    "3. Techniques for handling heterogeneous data\n",
    "4. Performance optimization methods\n",
    "5. Comprehensive troubleshooting guide\n",
    "\n",
    "These tools and techniques should help you better understand and optimize your Fed-MVKM implementations for real-world applications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d70e315",
   "metadata": {},
   "source": [
    "## 8.6 Implementation Guides\n",
    "\n",
    "### For Developers\n",
    "1. **REST API Implementation**\n",
    "   - Endpoint structure for client-server communication\n",
    "   - Authentication and authorization\n",
    "   - API versioning and documentation\n",
    "   - Error handling and logging\n",
    "   - Rate limiting and security measures\n",
    "\n",
    "### For Researchers\n",
    "1. **Experiment Setup**\n",
    "   - Data preprocessing pipeline\n",
    "   - Cross-validation strategies\n",
    "   - Metrics collection and analysis\n",
    "   - Ablation studies\n",
    "   - Reproducibility guidelines\n",
    "\n",
    "### For Industry Users\n",
    "1. **Production Deployment**\n",
    "   - System requirements and scaling\n",
    "   - Monitoring and alerting\n",
    "   - Backup and recovery\n",
    "   - Performance optimization\n",
    "   - Security best practices\n",
    "\n",
    "### Integration Guide\n",
    "1. **System Integration**\n",
    "   - Database integration\n",
    "   - Message queue setup\n",
    "   - Load balancing\n",
    "   - Caching strategies\n",
    "   - Service discovery\n",
    "\n",
    "### Maintenance Guide\n",
    "1. **System Maintenance**\n",
    "   - Update procedures\n",
    "   - Health checks\n",
    "   - Performance tuning\n",
    "   - Troubleshooting\n",
    "   - Documentation updates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbd0c104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: REST API Implementation with FastAPI\n",
    "from fastapi import FastAPI, HTTPException\n",
    "from pydantic import BaseModel\n",
    "from typing import List, Dict\n",
    "import numpy as np\n",
    "\n",
    "app = FastAPI()\n",
    "\n",
    "class ClientData(BaseModel):\n",
    "    client_id: str\n",
    "    views: List[List[List[float]]]\n",
    "    parameters: Dict\n",
    "\n",
    "@app.post(\"/train\")\n",
    "async def train_model(data: ClientData):\n",
    "    try:\n",
    "        # Convert data to numpy arrays\n",
    "        views = [np.array(view) for view in data.views]\n",
    "        \n",
    "        # Initialize model with client parameters\n",
    "        config = FedMVKMEDConfig(**data.parameters)\n",
    "        model = FedMVKMED(config)\n",
    "        \n",
    "        # Train on client data\n",
    "        results = model.fit({data.client_id: views})\n",
    "        \n",
    "        return {\n",
    "            \"status\": \"success\",\n",
    "            \"model_parameters\": model.get_parameters(),\n",
    "            \"metrics\": model.get_metrics()\n",
    "        }\n",
    "    except Exception as e:\n",
    "        raise HTTPException(status_code=500, detail=str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1987264",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: Research Experiment Setup\n",
    "class ExperimentManager:\n",
    "    def __init__(self, config, data_dir=\"./experiments\"):\n",
    "        self.config = config\n",
    "        self.data_dir = data_dir\n",
    "        self.results = {}\n",
    "        \n",
    "    def run_experiment(self, views, labels, n_clients=2, n_runs=5):\n",
    "        \"\"\"Run experiment multiple times and collect metrics.\"\"\"\n",
    "        for run in range(n_runs):\n",
    "            print(f\"\\nRun {run + 1}/{n_runs}\")\n",
    "            \n",
    "            # Create client partitions\n",
    "            client_data, client_labels = create_client_partitions(\n",
    "                views, labels, n_clients\n",
    "            )\n",
    "            \n",
    "            # Train model\n",
    "            model = FedMVKMED(self.config)\n",
    "            model.fit(client_data)\n",
    "            \n",
    "            # Collect metrics\n",
    "            predictions = model.predict(client_data)\n",
    "            all_pred = np.concatenate([pred for pred in predictions.values()])\n",
    "            metrics = MVKMEDMetrics.compute_metrics(views, all_pred, labels)\n",
    "            \n",
    "            self.results[f\"run_{run}\"] = {\n",
    "                \"metrics\": metrics,\n",
    "                \"model_state\": model.get_parameters(),\n",
    "                \"convergence\": model.history\n",
    "            }\n",
    "        \n",
    "        return self.analyze_results()\n",
    "    \n",
    "    def analyze_results(self):\n",
    "        \"\"\"Analyze experiment results.\"\"\"\n",
    "        metrics_summary = {}\n",
    "        \n",
    "        # Calculate mean and std of metrics\n",
    "        for metric in self.results[\"run_0\"][\"metrics\"].keys():\n",
    "            values = [run[\"metrics\"][metric] \n",
    "                     for run in self.results.values()]\n",
    "            metrics_summary[metric] = {\n",
    "                \"mean\": np.mean(values),\n",
    "                \"std\": np.std(values)\n",
    "            }\n",
    "        \n",
    "        return metrics_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d03f1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: Production Deployment Configuration\n",
    "class ProductionConfig:\n",
    "    def __init__(self):\n",
    "        self.config = {\n",
    "            \"system\": {\n",
    "                \"max_memory\": \"16G\",\n",
    "                \"num_workers\": 4,\n",
    "                \"gpu_enabled\": True,\n",
    "                \"log_level\": \"INFO\"\n",
    "            },\n",
    "            \"monitoring\": {\n",
    "                \"metrics_endpoint\": \"/metrics\",\n",
    "                \"health_check_interval\": 60,\n",
    "                \"alert_threshold\": 0.95\n",
    "            },\n",
    "            \"security\": {\n",
    "                \"ssl_enabled\": True,\n",
    "                \"api_key_required\": True,\n",
    "                \"rate_limit\": 100\n",
    "            },\n",
    "            \"backup\": {\n",
    "                \"backup_interval\": 3600,\n",
    "                \"max_backups\": 24,\n",
    "                \"backup_path\": \"/data/backups\"\n",
    "            }\n",
    "        }\n",
    "    \n",
    "    def validate_system_resources(self):\n",
    "        \"\"\"Validate system meets requirements.\"\"\"\n",
    "        import psutil\n",
    "        import torch\n",
    "        \n",
    "        # Check memory\n",
    "        memory = psutil.virtual_memory()\n",
    "        memory_gb = memory.total / (1024 ** 3)\n",
    "        required_gb = float(self.config[\"system\"][\"max_memory\"].rstrip(\"G\"))\n",
    "        \n",
    "        if memory_gb < required_gb:\n",
    "            raise ValueError(f\"Insufficient memory: {memory_gb:.1f}G < {required_gb}G\")\n",
    "        \n",
    "        # Check GPU if enabled\n",
    "        if self.config[\"system\"][\"gpu_enabled\"]:\n",
    "            if not torch.cuda.is_available():\n",
    "                raise ValueError(\"GPU required but not available\")\n",
    "        \n",
    "        return True\n",
    "    \n",
    "    def setup_monitoring(self):\n",
    "        \"\"\"Setup monitoring and alerting.\"\"\"\n",
    "        from prometheus_client import start_http_server, Summary\n",
    "        \n",
    "        # Create metrics\n",
    "        REQUEST_TIME = Summary(\n",
    "            'request_processing_seconds',\n",
    "            'Time spent processing request'\n",
    "        )\n",
    "        \n",
    "        # Start metrics endpoint\n",
    "        start_http_server(\n",
    "            port=8000,\n",
    "            addr='localhost'\n",
    "        )\n",
    "        \n",
    "        return REQUEST_TIME"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615f756e",
   "metadata": {},
   "source": [
    "## 8.6 Developer Implementation Guide\n",
    "\n",
    "### Key Implementation Features:\n",
    "\n",
    "1. **System Architecture Integration**\n",
    "   - REST API endpoints for client-server communication\n",
    "   - WebSocket support for real-time updates\n",
    "   - Docker containerization for easy deployment\n",
    "   - CI/CD pipeline integration\n",
    "\n",
    "2. **Error Handling and Logging**\n",
    "   - Comprehensive error tracking\n",
    "   - Performance monitoring\n",
    "   - Detailed logging for debugging\n",
    "   - System health metrics\n",
    "\n",
    "3. **Scalability Features**\n",
    "   - Horizontal scaling capabilities\n",
    "   - Load balancing configuration\n",
    "   - Database sharding strategies\n",
    "   - Caching mechanisms\n",
    "\n",
    "4. **Security Implementation**\n",
    "   - Authentication and authorization\n",
    "   - Data encryption in transit/rest\n",
    "   - Secure client communication\n",
    "   - Audit logging\n",
    "\n",
    "5. **Testing Framework**\n",
    "   - Unit test templates\n",
    "   - Integration test suites\n",
    "   - Performance test scenarios\n",
    "   - Security test cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f44dcd48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: Implementation of a REST API endpoint for Fed-MVKM\n",
    "from fastapi import FastAPI, HTTPException\n",
    "from pydantic import BaseModel\n",
    "\n",
    "app = FastAPI()\n",
    "\n",
    "class ClientData(BaseModel):\n",
    "    client_id: str\n",
    "    views: list\n",
    "    parameters: dict\n",
    "\n",
    "@app.post(\"/fedmvkm/update\")\n",
    "async def update_model(data: ClientData):\n",
    "    try:\n",
    "        # Process client update\n",
    "        client_update = process_client_update(data)\n",
    "        \n",
    "        # Update global model\n",
    "        global_update = update_global_model(client_update)\n",
    "        \n",
    "        return {\n",
    "            \"status\": \"success\",\n",
    "            \"global_parameters\": global_update\n",
    "        }\n",
    "    except Exception as e:\n",
    "        raise HTTPException(status_code=500, detail=str(e))\n",
    "\n",
    "def process_client_update(data):\n",
    "    \"\"\"Process updates from client\"\"\"\n",
    "    # Implementation details\n",
    "    return data\n",
    "\n",
    "def update_global_model(update):\n",
    "    \"\"\"Update global model with client update\"\"\"\n",
    "    # Implementation details\n",
    "    return update"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f9f253",
   "metadata": {},
   "source": [
    "## 8.7 Research Applications Guide\n",
    "\n",
    "### Research Features:\n",
    "\n",
    "1. **Experimental Setup**\n",
    "   - Dataset preparation protocols\n",
    "   - Parameter initialization methods\n",
    "   - Evaluation metrics selection\n",
    "   - Statistical analysis tools\n",
    "   - Cross-validation procedures\n",
    "\n",
    "2. **Algorithm Modifications**\n",
    "   - Custom objective functions\n",
    "   - Novel distance metrics\n",
    "   - Alternative optimization methods\n",
    "   - Convergence criteria variants\n",
    "   - Ensemble approaches\n",
    "\n",
    "3. **Comparative Analysis**\n",
    "   - Baseline implementations\n",
    "   - Performance benchmarks\n",
    "   - Complexity analysis\n",
    "   - Ablation studies\n",
    "   - Sensitivity analysis\n",
    "\n",
    "4. **Visualization Tools**\n",
    "   - Result plotting functions\n",
    "   - Interactive visualizations\n",
    "   - Cluster analysis tools\n",
    "   - Performance graphs\n",
    "   - Distribution plots\n",
    "\n",
    "5. **Documentation Requirements**\n",
    "   - Methodology description\n",
    "   - Result reporting templates\n",
    "   - Parameter justification\n",
    "   - Limitation analysis\n",
    "   - Future work suggestions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29756916",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: Research experiment setup and analysis\n",
    "\n",
    "class ExperimentManager:\n",
    "    def __init__(self, config):\n",
    "        self.config = config\n",
    "        self.results = {}\n",
    "        \n",
    "    def run_experiment(self, dataset, params):\n",
    "        \"\"\"Run a complete experiment with given parameters\"\"\"\n",
    "        # Initialize model\n",
    "        model = FedMVKMED(params)\n",
    "        \n",
    "        # Cross-validation\n",
    "        cv_scores = self.cross_validate(model, dataset)\n",
    "        \n",
    "        # Statistical analysis\n",
    "        stats = self.analyze_results(cv_scores)\n",
    "        \n",
    "        # Store results\n",
    "        self.results[params['experiment_id']] = {\n",
    "            'scores': cv_scores,\n",
    "            'statistics': stats\n",
    "        }\n",
    "        \n",
    "        return cv_scores, stats\n",
    "    \n",
    "    def cross_validate(self, model, dataset, k=5):\n",
    "        \"\"\"Perform k-fold cross-validation\"\"\"\n",
    "        from sklearn.model_selection import KFold\n",
    "        kf = KFold(n_splits=k, shuffle=True)\n",
    "        scores = []\n",
    "        \n",
    "        for train_idx, test_idx in kf.split(dataset):\n",
    "            # Train and evaluate model\n",
    "            train_score = self.train_and_evaluate(model, dataset, train_idx, test_idx)\n",
    "            scores.append(train_score)\n",
    "            \n",
    "        return scores\n",
    "    \n",
    "    def analyze_results(self, scores):\n",
    "        \"\"\"Perform statistical analysis of results\"\"\"\n",
    "        return {\n",
    "            'mean': np.mean(scores),\n",
    "            'std': np.std(scores),\n",
    "            'confidence_interval': self.compute_ci(scores)\n",
    "        }\n",
    "    \n",
    "    @staticmethod\n",
    "    def compute_ci(data, confidence=0.95):\n",
    "        \"\"\"Compute confidence interval\"\"\"\n",
    "        from scipy import stats\n",
    "        return stats.t.interval(confidence, len(data)-1,\n",
    "                              loc=np.mean(data),\n",
    "                              scale=stats.sem(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cfe808",
   "metadata": {},
   "source": [
    "## 8.8 Industry Applications Guide\n",
    "\n",
    "### Industry Implementation Features:\n",
    "\n",
    "1. **Production Deployment**\n",
    "   - System requirements\n",
    "   - Installation guides\n",
    "   - Configuration templates\n",
    "   - Monitoring setup\n",
    "   - Backup strategies\n",
    "\n",
    "2. **Performance Optimization**\n",
    "   - Resource utilization\n",
    "   - Throughput optimization\n",
    "   - Response time tuning\n",
    "   - Memory management\n",
    "   - Cost optimization\n",
    "\n",
    "3. **Integration Guidelines**\n",
    "   - API documentation\n",
    "   - Data pipeline setup\n",
    "   - Third-party integrations\n",
    "   - Legacy system compatibility\n",
    "   - Cloud deployment\n",
    "\n",
    "4. **Maintenance Procedures**\n",
    "   - Update protocols\n",
    "   - Backup procedures\n",
    "   - Recovery plans\n",
    "   - Version control\n",
    "   - Documentation updates\n",
    "\n",
    "5. **Support Resources**\n",
    "   - Troubleshooting guides\n",
    "   - FAQ documentation\n",
    "   - Support channels\n",
    "   - Training materials\n",
    "   - Best practices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "746bdaa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example: Production deployment configuration and monitoring\n",
    "\n",
    "class ProductionConfig:\n",
    "    def __init__(self):\n",
    "        self.config = self.load_config()\n",
    "        self.logger = self.setup_logging()\n",
    "        self.metrics = self.setup_metrics()\n",
    "    \n",
    "    @staticmethod\n",
    "    def load_config():\n",
    "        \"\"\"Load production configuration\"\"\"\n",
    "        import yaml\n",
    "        with open('config/production.yml', 'r') as f:\n",
    "            return yaml.safe_load(f)\n",
    "    \n",
    "    def setup_logging(self):\n",
    "        \"\"\"Configure logging for production\"\"\"\n",
    "        import logging\n",
    "        logger = logging.getLogger('FedMVKM')\n",
    "        logger.setLevel(logging.INFO)\n",
    "        \n",
    "        # Add handlers (file, console, etc.)\n",
    "        handler = logging.FileHandler('logs/fedmvkm.log')\n",
    "        handler.setFormatter(logging.Formatter(\n",
    "            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n",
    "        ))\n",
    "        logger.addHandler(handler)\n",
    "        \n",
    "        return logger\n",
    "    \n",
    "    def setup_metrics(self):\n",
    "        \"\"\"Configure metrics collection\"\"\"\n",
    "        from prometheus_client import Counter, Histogram\n",
    "        \n",
    "        metrics = {\n",
    "            'model_updates': Counter(\n",
    "                'fedmvkm_model_updates_total',\n",
    "                'Total number of model updates'\n",
    "            ),\n",
    "            'processing_time': Histogram(\n",
    "                'fedmvkm_processing_seconds',\n",
    "                'Time spent processing updates'\n",
    "            )\n",
    "        }\n",
    "        \n",
    "        return metrics\n",
    "    \n",
    "    def monitor_performance(self, func):\n",
    "        \"\"\"Decorator for performance monitoring\"\"\"\n",
    "        def wrapper(*args, **kwargs):\n",
    "            with self.metrics['processing_time'].time():\n",
    "                result = func(*args, **kwargs)\n",
    "                self.metrics['model_updates'].inc()\n",
    "                return result\n",
    "        return wrapper"
   ]
  }
 ],
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